Summary

Row

confirmed

11,350

death

206 (1.8%)

recovered

4,357 (38.4%)

Row

Daily cumulative cases by type

Comparison

Column

Daily new confirmed cases

Cases distribution by type

Map

World map of cases (use + and - icons to zoom in/out)

About

The Coronavirus Dashboard: the case of South Africa

This Coronavirus dashboard: the case of South Africa provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for South Africa. This dashboard is built with R using the R Markdown framework and was adapted from this dashboard by Rami Krispin.

Code

The code behind this dashboard is available on GitHub.

Data

The input data for this dashboard is the dataset available from the {coronavirus} R package. Make sure to download the development version of the package to have the latest data:

install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.

Information

More information about this dashboard (and how to replicate it for your own country) can be found in this article.

Update

The data is as of Tuesday May 12, 2020 and the dashboard has been updated on Friday June 26, 2020.

Based on a tutorial from www.statsandr.com.

---
title: "Coronavirus in South Africa"
author: "De Villiers Neethling"
date: "25/06/2020"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
    vertical_layout: fill 
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
#data(coronavirus)
#View(coronavirus)
#max(coronavirus$date)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color

# Set icons
# https://fontawesome.com/v4.7.0/icons

active_color <- "#1f77b4"
recovered_color <- "forestgreen"
confirmed_color <- "orange"
death_color <- "tomato"
fatherland = "South Africa"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == fatherland) %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>%
  dplyr::filter(country == fatherland) %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )


df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-ambulance",
  color = confirmed_color
)
```


### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Deaths (case fatality rate)",
  icon = "fas fa-minus-circle",
  color = death_color
)
```

### recovered
```{r}

valueBox(
 value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Recovered (percentage)",
  icon = "fas fa-heartbeat",
  color = recovered_color
)
```



Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type**
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Confirmed",
    line = list(color = confirmed_color),
    marker = list(color = confirmed_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~recovered_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Recovered",
    line = list(color = recovered_color),
    marker = list(color = recovered_color)
  ) %>%
  
  plotly::add_annotations(
    x = as.Date("2020-03-05"),
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-27"),
    y = 3,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-27"),
    y = 14,
    text = paste(
      "Lockdown"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  
  # fig.show(config={"displayModeBar": False, "showTips": False}) 
  plotly::config(
    displayModeBar = FALSE
  ) %>%
  
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Comparison
=======================================================================


Column {data-width=400}
-------------------------------------


### **Daily new confirmed cases**
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::mutate(country = country) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

#----------------------------------------
# Plotting the data

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~fatherland,
    type = "scatter",
    mode = "lines+markers",
    name = fatherland
  ) %>%
  # plotly::add_trace(
  #   x = ~date,
  #   y = ~France,
  #   type = "scatter",
  #   mode = "lines+markers",
  #   name = "France"
  # ) %>%
  # plotly::add_trace(
  #   x = ~date,
  #   y = ~Spain,
  #   type = "scatter",
  #   mode = "lines+markers",
  #   name = "Spain"
  # ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Botswana,
    type = "scatter",
    mode = "lines+markers",
    name = "Botswana"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Namibia,
    type = "scatter",
    mode = "lines+markers",
    name = "Namibia"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Mozambique,
    type = "scatter",
    mode = "lines+markers",
    name = "Mozambique"
  ) %>%
  # They claim to have Lesotho data, but apparently not dailies
  # plotly::add_trace(
  #   x = ~date,
  #   y = ~Lesotho,
  #   type = "scatter",
  #   mode = "lines+markers",
  #   name = "Lesotho"
  # ) %>%
  
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "New confirmed cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )


```
 
### **Cases distribution by type**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Belgium" |
    country == "France" |
    country == "Italy" |
    country == "Spain") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

plotly::plot_ly(
  data = df_EU,
  x = ~country,
  y = ~ confirmed,
  type = "bar",
  name = "Confirmed",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Map
=======================================================================

### **World map of cases** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(country, province, lat, long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~long, lat = ~lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "country", "province")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





About
=======================================================================

**The Coronavirus Dashboard: the case of South Africa**

This [Coronavirus dashboard: the case of South Africa](https://github.com/DevilEars/Optics/blob/master/R_Projects/Covid-19%20Dashboard/coronavirusdashboard.Rmd) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for South Africa. This dashboard is built with R using the R Markdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/DevilEars/Optics/blob/master/R_Projects/Covid-19%20Dashboard/coronavirusdashboard.Rmd){target="_blank"}.

**Data**

The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:

```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.

**Information**

More information about this dashboard (and how to replicate it for your own country) can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.


*Based on a tutorial from [www.statsandr.com](https://www.statsandr.com/)*.